An experimental investigation on the innate relationship between quality and refactoring

Bavota, Gabriele; Lucia, Andrea De; Penta, Massimiliano Di; Oliveto, Rocco; Palomba, Fabio

doi:10.1016/j.jss.2015.05.024

Cited by 198 publications

(241 citation statements)

References 44 publications

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“…Regarding the operations reducing the level of intensity, Table 7 reports the percentage of the different types of changes, i.e., New Feature (NF), Bug Fixing (BF), Refactoring (R), and Enhancement (E), applied to the set of textual and structural code smells in our dataset. Considering the results achieved in previous work [10], [22], [52], [81], [94], the most unexpected result is the one related to the percentage of refactoring operations. In fact, even if the number of refactoring operations performed on code smells remains quite lowconfirming that code smells are poorly refactored-we observed that textual code smells are generally more prone to be subject to these operations (Blob=+4%, Feature Envy=+6%, Long Method=+7%, Misplaced Class=+9%, Promiscuous Package=+10%).…”

Section: Analysis Of the Resultsmentioning

confidence: 83%

“…Afterwards, each participant was required to inspect a total of twelve code elements related to one of the three projects in our study, namely five pairs of instances 10. http://www.esurveyspro.com The experiment was composed of six consecutive sessions with two tasks each.…”

Section: Empirical Study Definition and Designmentioning

confidence: 99%

“…However, recent studies have indicated that code smells detected by existing tools are generally ignored (and thus not refactored) by the developers [4], [10], [94]. A possible reason is that developers do not perceive the code smells identified by the tool as actual design problems or, if they do, they are not able to practically work on such code smells.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

Palomba

Panichella

Zaidman

et al. 2018

IIEEE Trans. Software Eng.

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Abstract-Code smells are symptoms of poor design or implementation choices that have a negative effect on several aspects of software maintenance and evolution, such as program comprehension or change-and fault-proneness. This is why researchers have spent a lot of effort on devising methods that help developers to automatically detect them in source code. Almost all the techniques presented in literature are based on the analysis of structural properties extracted from source code, although alternative sources of information (e.g., textual analysis) for code smell detection have also been recently investigated. Nevertheless, some studies have indicated that code smells detected by existing tools based on the analysis of structural properties are generally ignored (and thus not refactored) by the developers. In this paper, we aim at understanding whether code smells detected using textual analysis are perceived and refactored by developers in the same or different way than code smells detected through structural analysis. To this aim, we set up two different experiments. We have first carried out a software repository mining study to analyze how developers act on textually or structurally detected code smells. Subsequently, we have conducted a user study with industrial developers and quality experts in order to qualitatively analyze how they perceive code smells identified using the two different sources of information. Results indicate that textually detected code smells are easier to identify and for this reason they are considered easier to refactor with respect to code smells detected using structural properties. On the other hand, the latter are often perceived as more severe, but more difficult to exactly identify and remove.

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Section: Analysis Of the Resultsmentioning

confidence: 83%

Section: Empirical Study Definition and Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

Palomba

Panichella

Zaidman

et al. 2018

IIEEE Trans. Software Eng.

Self Cite

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show abstract

“…Results also show that, even when developers are aware of the presence of the smell, they do not refactor it. Bavota et al (2015) analyzed 13,000 refactorings in three software systems, and noticed that 40% of the refactorings were applied on smelly classes. On the other hand, in only 7% of the cases the smells were removed.…”

Section: Developers' Perceptions Of Code Smellsmentioning

confidence: 99%

Code smells for Model-View-Controller architectures

et al. 2017

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Previous studies have shown the negative effects that low-quality code can have on maintainability proxies, such as code change-and defect-proneness. One of the symptoms of low-quality code are code smells, defined as sub-optimal implementation choices. While this definition is quite general and seems to suggest a wide spectrum of smells that can affect software systems, the research literature mostly focuses on the set of smells defined in the catalog by Fowler and Beck, reporting design issues that can potentially affect any kind of system, regardless of their architecture (e.g., Complex Class). However, systems adopting a specific architecture (e.g., the Model-View-Controller pattern) can be affected by other types of poor practices that only manifest themselves in the chosen architecture. We present a catalog of six smells tailored to MVC applications and defined by surveying/interviewing 53 MVC developers. We validate our catalog from different perspectives. First, we assess the relationship between the defined smells and the code change-and defect-proneness. Second, we investigate when these smells are introduced and how long they survive. Third, we survey 21 developers to verify their perception of the defined smells. Fourth, since our catalog has been mainly defined together with developers adopting a specific Java framework in their MVC applications (e.g., Spring), we interview four expert developers working with different technologies for the implementation of their MVC applications to check the generalizability of our catalog. The achieved results show that the defined Web MVC smells (i) more often than not, have more chances of being subject to changes and defects, (ii) are mostly introduced when the affected file (i.e., the file containing the smell) is committed for the first time in the repository and survive for long time in the system, (iii) are perceived by developers as severe problems, and (iv) generalize to other languages/frameworks.

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“…Recent studies by Bavota et al [9] investigate possible relationships between metrics, code smells and developer refactoring activities. They mined the evolution history of three Java open source projects to investigate whether refactoring activities occur on code components for which certain indicators such as quality metrics or the presence of smells as detected by tools suggest there might be need for refactoring operations.…”

Section: Related Workmentioning

confidence: 99%

Search-Based Refactoring: Metrics Are Not Enough

Simons

Singer

White

2015

Search-Based Software Engineering

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Abstract. Search-based Software Engineering (SBSE) techniques have been applied extensively to refactor software, often based on metrics that describe the object-oriented structure of an application. Recent work shows that in some cases applying popular SBSE tools to open-source software does not necessarily lead to an improved version of the software as assessed by some subjective criteria. Through a survey of professionals, we investigate the relationship between popular SBSE refactoring metrics and the subjective opinions of software engineers. We find little or no correlation between the two. Through qualitative analysis, we find that a simple static view of software is insufficient to assess software quality, and that software quality is dependent on factors that are not amenable to measurement via metrics. We recommend that future SBSE refactoring research should incorporate information about the dynamic behaviour of software, and conclude that a human-in-the-loop approach may be the only way to refactor software in a manner helpful to an engineer.

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An experimental investigation on the innate relationship between quality and refactoring

Cited by 198 publications

References 44 publications

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

Code smells for Model-View-Controller architectures

Search-Based Refactoring: Metrics Are Not Enough

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